docstr: Calculate the loss based on the features extracted by the detection
  head.
  
  Args:
      cls_scores (list[Tensor]): Box scores for each scale level
          Has shape (N, num_anchors * num_classes, H, W)
      bbox_preds (list[Tensor]): Box energies / deltas for each scale
          level with shape (N, num_anchors * 4, H, W)
      centernesses (list[Tensor]): Centerness for each scale
          level with shape (N, num_anchors * 1, H, W)
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance.  It usually includes ``bboxes`` and ``labels``
          attributes.
      batch_img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
          Batch of gt_instances_ignore. It includes ``bboxes`` attribute
          data that is ignored during training and testing.
          Defaults to None.
  
  Returns:
      dict[str, Tensor]: A dictionary of loss components.
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code: def loss_by_feat(
      self,
      cls_scores: List[Tensor],
      bbox_preds: List[Tensor],
      centernesses: List[Tensor],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
  ) -> dict:
      """Calculate the loss based on the features extracted by the detection
      head.
  
      Args:
          cls_scores (list[Tensor]): Box scores for each scale level
              Has shape (N, num_anchors * num_classes, H, W)
          bbox_preds (list[Tensor]): Box energies / deltas for each scale
              level with shape (N, num_anchors * 4, H, W)
          centernesses (list[Tensor]): Centerness for each scale
              level with shape (N, num_anchors * 1, H, W)
          batch_gt_instances (list[:obj:`InstanceData`]): Batch of
              gt_instance.  It usually includes ``bboxes`` and ``labels``
              attributes.
          batch_img_metas (list[dict]): Meta information of each image, e.g.,
              image size, scaling factor, etc.
          batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
              Batch of gt_instances_ignore. It includes ``bboxes`` attribute
              data that is ignored during training and testing.
              Defaults to None.
  
      Returns:
          dict[str, Tensor]: A dictionary of loss components.
      """
      featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
      assert len(featmap_sizes) == self.prior_generator.num_levels
  
      device = cls_scores[0].device
      anchor_list, valid_flag_list = self.get_anchors(
          featmap_sizes, batch_img_metas, device=device
      )
  
      cls_reg_targets = self.get_targets(
          anchor_list,
          valid_flag_list,
          batch_gt_instances,
          batch_img_metas,
          batch_gt_instances_ignore=batch_gt_instances_ignore,
      )
  
      (
          anchor_list,
          labels_list,
          label_weights_list,
          bbox_targets_list,
          bbox_weights_list,
          avg_factor,
          ori_anchors,
          ori_labels,
          ori_bbox_targets,
      ) = cls_reg_targets
  
      avg_factor = reduce_mean(
          torch.tensor(avg_factor, dtype=torch.float, device=device)
      ).item()
  
      (
          losses_cls,
          losses_bbox,
          loss_centerness,
          bbox_avg_factor,
      ) = multi_apply(
          self.loss_by_feat_single,
          anchor_list,
          cls_scores,
          bbox_preds,
          centernesses,
          labels_list,
          label_weights_list,
          bbox_targets_list,
          avg_factor=avg_factor,
      )
  
      bbox_avg_factor = sum(bbox_avg_factor)
      bbox_avg_factor = (
          reduce_mean(bbox_avg_factor).clamp_(min=1).item()
      )
      losses_bbox = list(
          map(lambda x: x / bbox_avg_factor, losses_bbox)
      )
  
      # diff
      pos_coords = (ori_anchors, ori_labels, ori_bbox_targets, "atss")
      return dict(
          loss_cls=losses_cls,
          loss_bbox=losses_bbox,
          loss_centerness=loss_centerness,
          pos_coords=pos_coords,
      )
  
file_path: mmdetection/projects/CO-DETR/codetr/co_atss_head.py
imports: from typing import List
  import torch
  from torch import Tensor
  from mmdet.models.dense_heads import ATSSHead
  from mmdet.models.utils import images_to_levels, multi_apply
  from mmdet.registry import MODELS
  from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
label_desc: Use image node_id 1 for calling the CoATSSHead obj's underlying member method code with example usage: obj.loss_by_feat(cls_scores, bbox_preds, centernesses, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore) and returns: dict